MétaCan
Menu
Back to cohort
Record W83595211

Chapter 15 – The Epigenetics of Suicide: The Critical Impact of the Environment on Epigenetic Regulation in Suicide

2014· book-chapter· en· W83595211 on OpenAlexaff
Benoît Labonté, Pierre-Éric Lutz, Gustavo Turecki

Bibliographic record

VenueAcademic Press eBooks · 2014
Typebook-chapter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsDouglas Mental Health University Institute
Fundersnot available
KeywordsEpigeneticsDNA methylationMood disordersPsychologyMoodNeglectVulnerability (computing)Depression (economics)Clinical psychologyPsychiatryGeneticsBiologyGeneGene expressionAnxiety
DOInot available

Abstract

fetched live from OpenAlex

Suicide is a problem resulting from an interaction among several factors. Among these factors, early-life adversity, characterized by child sexual and physical abuse as well as parental neglect, is one of the strongest risk factors for depression and suicidal behaviors. Although it is clear that child abuse increases the risk for depression and suicide, the mechanisms mediating these effects are still unknown. Recent evidence suggests that epigenetic mechanisms may be involved in mediating the effects of early-life adversity on behavior. Early-life adversity in humans and animals has been shown to alter DNA methylation in genes regulatory regions which, in turn, has been associated with changes in gene expression and behavioral modifications. However, although epigenetic modifications have been found in several genes, the extent of epigenetic changes induced by early-life adversity is still unknown and their impacts on increasing suicide risk are unclear. This chapter aims at identifying and describing the molecular mechanisms by which early-life adversity induces behavioral changes conferring vulnerability toward mood disorders and suicidal behaviors.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.482
Threshold uncertainty score0.850

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.037
GPT teacher head0.302
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueAcademic Press eBooksSame topicEpigenetics and DNA MethylationFrench-language works237,207